摘要
积雪深度不仅用于研究地表辐射平衡,还可以研究积雪的水文效应,是天气和水文模型运行的必要参数,同时,积雪深度监测在融雪径流预报、水资源管理以及洪水控制方面都具有重要作用。我国现有积雪深度反演算法所依据的站点数据主要分布在我国中部、东部、南部,而在西北的新疆地区站点数据相对较少,因此造成了现有算法在新疆地区的雪深反演精度较差。选择新疆地区作为研究区,以FY3B-MWRI为数据源,根据该地区的地形特征和地面土地覆盖类型特征,利用回归分析方法,研究了该区域内林地、农田和草地3种土地覆盖类型的积雪深度反演算法,并结合地面实测积雪深度数据,对算法精度进行验证。结果显示,林地、农田和草地3种土地覆盖类型的雪深反演结果的R2与RMSE分别为0.758,2.58、0.729,3.21、0.854,5.70,表明该算法对新疆地区积雪深度反演得到了较高的反演精度。
The snow depth is one of the necessary parameters of weather and hydrological model,which is not only used to study the balance of surface radiation,but also to study the hydrological effects of snow.Meanwhile,the snow depth monitoring plays an important role in snow-melt runoff forecasting,water resources management and flood control.the sites to measure snow depth data to constructe existing snow depth retrieval models based on FY3B-MWRI data mainly distribute in middle,east and south of China,and the sites in Xinjiang region are relative less.Thus,it causes the poor precision of the snow depth retrieval algorithm in the Xinjiang region.In this paper,we select Xinjiang region as the study area and select FY3B-MWRI as the data source.According to the topography characteristics and surface land cover characteristics of the region,we use regression analysis method to study the different snow depth retrieval algorithms of the three land covertypes (forestland,farmland and grassland).Then we verify the accuracy of the algorithms comparing to the field measured data.The results show that the R2 and RMSE of the three land covertypes are 0.758,2.58,0.729,3.21,0.854,5.70 respectively,so the algorithms of this paper have a high accuracy of the snow depth retrieval in Xinjiang region.
作者
李长春
徐轩
包安明
刘雪峰
杨文攀
Li Changchun;Xu Xuan;Bao Anming;Liu Xuefeng;Yang Wenpan(School of Surveying and land Information Engineering ,Henan Polytechnic University, Jiaozuo 454000,China;Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences ,Urumqi 830011,China;Department of Geography and Resource Management,The Chinese University of Hongkong, Hongkong 999077 ,China)
出处
《遥感技术与应用》
CSCD
北大核心
2018年第6期1030-1036,共7页
Remote Sensing Technology and Application
基金
国家自然科学基金项目(41371419)
新疆维吾尔自治区重点实验室项目(XJYS0205-2013-01)资助.
